3 papers
stat.ML2025
Exact characterization of ε-Safe Decision Regions for exponential family distributions and Multi Cost SVM approximation
Alberto Carlevaro, Teodoro Alamo, Fabrizio Dabbene +1
Probabilistic guarantees on the prediction of data-driven classifiers are necessary to define models that can be considered reliable. This is a key requirement for modern machine l…
stat.ML2024
Conformal Predictions for Probabilistically Robust Scalable Machine Learning Classification
Alberto Carlevaro, Teodoro Alamo Cantarero, Fabrizio Dabbene +1
Conformal predictions make it possible to define reliable and robust learning algorithms. But they are essentially a method for evaluating whether an algorithm is good enough to be…
stat.ML2023
Probabilistic Safety Regions Via Finite Families of Scalable Classifiers
Alberto Carlevaro, Teodoro Alamo, Fabrizio Dabbene +1
Supervised classification recognizes patterns in the data to separate classes of behaviours. Canonical solutions contain misclassification errors that are intrinsic to the numerica…